Single-cell analysis is trendy for good reasons: It has enabled asking and answering important questions. Of course, the substantive reasons are surrounded by much hype. Sometimes colleagues tell me they want to add single-cell RNA-seq analysis since it will help them publish their paper in a more prestigious journal, and sadly there is perhaps more truth to that than I want to believe.

On the other end of the spectrum, some colleagues from the mass-spec community are puzzled by our efforts to develop methods for single-cell mass-spec analysis: At HUPO, I have been repeatedly asked: “Why analyze single cells when you can identify more peptides in bulk samples?”

So, when do we need single-cell analysis? Can’t we just FACS sort cells based on markers and analyze the sorted cells? Indeed, that maybe a good strategy when the cells we analyze fall into relatively homogenous clusters (they will never be perfectly homogeneous) and we have a reliable marker for each cluster. If these assumptions hold, the averaging out of differences between individual cells will give us very useful coarse graining. Unfortunately, bulk analysis of the sorted cells cannot validate the assumption of homogeneity. For example, we can easily sort B-cells and T-cells from blood samples because we have well-defined markers for each cell type. However, the bulk analysis of the sorted cells will not provide any information on the homogeneity of the sorted T-cells. Yet, a wealth of single-cell analysis has demonstrated the existence of multiple states within T-cell subpopulations, states for which we rarely have well-defined markers allowing efficient FACS sorting and follow up bulk analysis.

FACS sorting is especially inadequate when the cell heterogeneity is not easily captured by discrete subpopulations / clusters of cells. For example the continuous gradient of macrophage states that we recently observed in our SCoPE2 data:  

To explore the heterogeneity within the macrophage-like cells, we sorted them based on the Laplacian vector. See Specht et al., 2019 for details.

In some cases, e.g., analysis of small clonal populations, the benefits of single-cell analysis may be too small to justify the increased cost. Sometimes, we can gain single-cell information from analyzing small groups of cells, e.g., Shaffer et al., 2018. Sometimes, nobody can be sure if single-cell analysis is needed. If we assume it’s needed and perform it, the data can refute our assumption and show us that there is no much heterogeneity, at least at the level of what we could measure. If we assume that there is no heterogeneity and thus no need for single cell analysis, e.g., FACS sort T-cells, the bulk analysis of the sorted cells will not correct our assumption. We can feel the assumption is validated while being blinded to what might be the most meaningful cellular diversity in the system. So, single-cell analysis is not always needed, but it is much better at correcting our assumptions and teaching us if it is needed or not.